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Commit a1bdefc8ddad authored by Ashwin Vishnu's avatar Ashwin Vishnu
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Fix typos

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......@@ -40,7 +40,7 @@
productivity. There are strong open-source communities using Python and a rich
scientific ecosystem of efficient libraries.
[^3]: See for exemple the [TIOBE Index](https://www.tiobe.com/tiobe-index/),
[^3]: See for example the [TIOBE Index](https://www.tiobe.com/tiobe-index/),
the [IEEE Spectrum
ranking](https://spectrum.ieee.org/computing/software/the-top-programming-languages-2019),
[Github reports](https://octoverse.github.com/) or the [Stack Overflow Annual
......@@ -55,7 +55,7 @@
execution) \cite{aycock2003brief}. Python also has AOT and JIT compilers.
However, the most standard way to execute Python code is to interpret it with a
program called CPython. It is the reference implementation of the language and
in 2020, it still does not have a builtin JIT compiler. Therefore, CPython is
in 2020, it still does not have a built-in JIT compiler. Therefore, CPython is
relatively slow which explains Zwart's results. However, it is important to
realize that this inefficiency of the interpreter has a weak effect on the
overall performance of most programs. Total elapsed time and energy
......@@ -74,7 +74,7 @@
However, some algorithms require low-level code and explicit loops. For
example, for the N-Body problem, the computation of the acceleration of each
particle involves a loop over all other particles. Few lines of code are repeated
$N^2/2$ times per timestep. Zwart (2020) considered 10000 timesteps and
$N^2/2$ times per time-step. Zwart (2020) considered 10000 time-steps and
$N=16384$, so the program is dominated by 1,342,177,280,000 executions of a
simple and inexpensive computation. Using CPython for this very hot loop makes
the whole program very inefficient. Good news for Python: it is straightforward
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